hepplerj

#!/usr/bin/env python3 

import requests 
from bs4 import BeautifulSoup

url = "https://archive.org/stream/ahandbooksocial00blisgoog/ahandbooksocial00blisgoog_djvu.txt"
response = requests.get(url)

soup = BeautifulSoup(response.text, "html.parser")
pre_selector = "#maincontent > div > pre"

hepplerj

require "webrick"

server = WEBrick::HTTPServer.new(:Port => 8000, :DocumentRoot => Dir.pwd)
trap("INT") { server.stop }
server.start

hepplerj

def bubble_sort(arr)
  for i in 0...arr.length
    sorted = true

    for k in 0...(arr.length - i - 1)
      if arr[k] > arr[k + 1]
        arr[k], arr[k + 1] = arr[k + 1], arr[k]
        sorted = false
      end
    end

hepplerj

#!/usr/bin/python3

import tweepy 
import csv

consumer_key = ""
consumer_secret = ""

access_token =  ""
access_token_secret = ""

hepplerj

library(tidycensus)
library(tidyverse)

# If not set, un-comment below and install your Census API key (https://api.census.gov/data/key_signup.html)
# census_api_key("YOUR KEY HERE", install = TRUE)

get_acs(geography = "metropolitan statistical area/micropolitan statistical area", 
        variables = "DP03_0021PE", 
        summary_var = "B01003_001", 
        survey = "acs1", 

hepplerj

library(tidyverse)
library(tidycensus)

# My recommendation is to use the tidycensus library to make getting this data
# easier than reading in the data from the Census website. 
#
# Before you can begin, you'll need to get an API key from the Census Bureau.
# You can acquire one here: 
#
# Once you have the API key, run the following in RStudio: 

hepplerj

library(charlatan)
library(salty)
library(magrittr)
library(readr)

messydata <- ch_generate('name','job','phone_number', n = 200)

messydata <- messydata %>% 
  mutate(job = salt_capitalization(job)) %>% 
  mutate(phone_number = salt_na(phone_number)) %>% 

hepplerj

library(tidyverse)
library(readxl)

data <- readxl::read_xlsx("data.xlsx")
reshaped <- data %>% gather(word, freq, 2:21)
reshaped <- reshaped %>% drop_na()

cleaned <- reshaped %>% 
  uncount(freq)

hepplerj

library(tidyverse)
library(maps)
library(mapdata)

data <- read_csv("~/Desktop/nplsuperfund.csv")
names(data) <- c("lat","lon","date")

# Filter down to USA extent to remove extraneous points
tidy <- data %>% 
  filter(lat < -67, lat > -125) %>% 

hepplerj

library(hexSticker)
library(tidyverse)
library(tidycensus)
library(sf)
library(viridis)

options(tigris_use_cache = TRUE)

nebraska_raw <- get_acs(state = "NE",
                        geography = "tract",